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Learning Choice Functions via Pareto-Embeddings

arXiv.org Machine Learning

We consider the problem of learning to choose from a given set of objects, where each object is represented by a feature vector. Traditional approaches in choice modelling are mainly based on learning a latent, real-valued utility function, thereby inducing a linear order on choice alternatives. While this approach is suitable for discrete (top-1) choices, it is not straightforward how to use it for subset choices. Instead of mapping choice alternatives to the real number line, we propose to embed them into a higher-dimensional utility space, in which we identify choice sets with Pareto-optimal points. To this end, we propose a learning algorithm that minimizes a differentiable loss function suitable for this task. We demonstrate the feasibility of learning a Pareto-embedding on a suite of benchmark datasets.


Meet AI's Multitool: Vector Embeddings - Liwaiwai

#artificialintelligence

Embeddings are one of the most versatile techniques in machine learning, and a critical tool every ML engineer should have in their toolbelt. It’s a shame, then, that so few of us understand what they are and what they’re good for! The problem, perhaps, is that embeddings sound slightly abstract and esoteric: In machine learning, an embedding ...


How to Remove Gender Bias in Machine Learning Models: NLP and Word Embeddings

#artificialintelligence

Most word embeddings used are glaringly sexist, let us look at some ways to de-bias such embeddings. Note - This article provides a review and the arguments made by Bolukbasi et al. in the paper "Man is to Computer Programmer as Woman is to Homemaker? All graphical drawings are made using draw.io. Word Embeddings are the core of NLP applications, and often, they end up being biased towards a gender due to the inherent stereotype present in the large text corpora they are trained on. Such models, when deployed to production can result in further widening of gender inequality and can have far fetched consequences on our society as a whole. To get a gist of what I'm talking about, here is a snippet from Bolukbasi et al., 2016 "Man is to Computer Programmer as Woman is to Homemaker?


Memotion Analysis through the Lens of Joint Embedding

arXiv.org Artificial Intelligence

Joint embedding (JE) is a way to encode multi-modal data into a vector space where text remains as the grounding key and other modalities like image are to be anchored with such keys. Meme is typically an image with embedded text onto it. Although, memes are commonly used for fun, they could also be used to spread hate and fake information. That along with its growing ubiquity over several social platforms has caused automatic analysis of memes to become a widespread topic of research. In this paper, we report our initial experiments on Memotion Analysis problem through joint embeddings. Results are marginally yielding SOTA.